This adds a parameter to the storage selection interface which allows
query engine(s) to pass information about the operations surrounding a
data selection.
This can for example be used by remote storage backends to infer the
correct downsampling aggregates that need to be provided.
* TotalFoo suggested a comprehensive timing, but TotalEvalTime was part
of the Exec timings, together with Queue timings
* The other option was to rename ExecTotalTime to TotalExecTime, but
there was already ExecQueueTime, suggesting Exec to be some sort of
group
API consumers should be able to get insight into the query run times.
The UI currently measures total roundtrip times. This PR allows for more
fine grained metrics to be exposed.
* adds new timer for total execution time (queue + eval)
* expose new timer, queue timer, and eval timer in stats field of the
range query response:
```json
{
"status": "success",
"data": {
"resultType": "matrix",
"result": [],
"stats": {
"execQueueTimeNs": 4683,
"execTotalTimeNs": 2086587,
"totalEvalTimeNs": 2077851
}
}
}
```
* stats field is optional, only set when query parameter `stats` is not
empty
Try it via
```sh
curl 'http://localhost:9090/api/v1/query_range?query=up&start=1486480279&end=1486483879&step=14000&stats=true'
```
Review feedback
* moved query stats json generation to query_stats.go
* use seconds for all query timers
* expose all timers available
* Changed ExecTotalTime string representation from Exec queue total time to Exec total time
This means that if there is no stale marker, only the usual staleness
delta (5m) applies.
It has occured to me that there is an oddity in the heurestic. It works
fine as long as you have 2 points within the last 5m, but breaks down
when the time window advances to the point where you have just 1 point.
Consider you had points at t=0 and t=10. With the heurestic it goes stale
at t=51, up until t=300. However from t=301 until t=310 we only
see the t=10 point and the series comes back to life. That is not
desirable.
I don't see a way to keep this form of heurestic working given this
issue, so thus I'm removing it.
* Re-add contexts to storage.Storage.Querier()
These are needed when replacing the storage by a multi-tenant
implementation where the tenant is stored in the context.
The 1.x query interfaces already had contexts, but they got lost in 2.x.
* Convert promql.Engine to use native contexts
With the squaring of the timestamp, we run into the
limitations of the 53bit mantissa for a 64bit float.
By subtracting away a timestamp of one of the samples (which is how the
intercept is used) we avoid this issue in practice as it's unlikely
that it is used over a very long time range.
Fixes#2674
* Fix error where we look into the future.
So currently we are adding values that are in the future for an older
timestamp. For example, if we have [(1, 1), (150, 2)] we will end up
showing [(1, 1), (2,2)].
Further it is not advisable to call .At() after Next() returns false.
Signed-off-by: Goutham Veeramachaneni <cs14btech11014@iith.ac.in>
* Retuen early if done
Signed-off-by: Goutham Veeramachaneni <cs14btech11014@iith.ac.in>
* Handle Seek() where we reach the end of iterator
Signed-off-by: Goutham Veeramachaneni <cs14btech11014@iith.ac.in>
* Simplify code
Signed-off-by: Goutham Veeramachaneni <cs14btech11014@iith.ac.in>
To cover the cases where stale markers may not be available,
we need to infer the interval and mark series stale based on that.
As we're lacking stale markers this is less accurate, however
it should be good enough for these cases.
We need 4 intervals as if say we had data at t=0 and t=10,
coming via federation. The next data point should be at t=20 however it
could take up to t=30 for it actually to be ingested, t=40 for it to be
scraped via federation and t=50 for it to be ingested.
We then add 10% on to that for slack, as we do elsewhere.
For instant vectors, if "stale" is the newest sample
ignore the timeseries.
For range vectors, filter out "stale" samples.
Make it possible to inject "stale" samples in promql tests.
Query and query_range should return the timestamp
at which an evaluation is performed, not the timestamp
of the data. This is as that's what query range asked
for, and we need to keep query consistent with that.
Query for a matrix remains unchanged, returning the literal
matrix.
Make the timestamp of instant vectors be the timestamp of the sample
rather than the evaluation. We were not using this anywhere, so this is
safe.
Add a function to return the timestamp of samples in an instant vector.
Fixes#1557
* Use request.Context() instead of a global map of contexts.
* Add some basic opentracing instrumentation on the query path.
* Remove tracehandler endpoint.
* Force buckets in a histogram to be monotonic for quantile estimation
The assumption that bucket counts increase monotonically with increasing
upperBound may be violated during:
* Recording rule evaluation of histogram_quantile, especially when rate()
has been applied to the underlying bucket timeseries.
* Evaluation of histogram_quantile computed over federated bucket
timeseries, especially when rate() has been applied
This is because scraped data is not made available to RR evalution or
federation atomically, so some buckets are computed with data from the N
most recent scrapes, but the other buckets are missing the most recent
observations.
Monotonicity is usually guaranteed because if a bucket with upper bound
u1 has count c1, then any bucket with a higher upper bound u > u1 must
have counted all c1 observations and perhaps more, so that c >= c1.
Randomly interspersed partial sampling breaks that guarantee, and rate()
exacerbates it. Specifically, suppose bucket le=1000 has a count of 10 from
4 samples but the bucket with le=2000 has a count of 7, from 3 samples. The
monotonicity is broken. It is exacerbated by rate() because under normal
operation, cumulative counting of buckets will cause the bucket counts to
diverge such that small differences from missing samples are not a problem.
rate() removes this divergence.)
bucketQuantile depends on that monotonicity to do a binary search for the
bucket with the qth percentile count, so breaking the monotonicity
guarantee causes bucketQuantile() to return undefined (nonsense) results.
As a somewhat hacky solution until the Prometheus project is ready to
accept the changes required to make scrapes atomic, we calculate the
"envelope" of the histogram buckets, essentially removing any decreases
in the count between successive buckets.
* Fix up comment docs for ensureMonotonic
* ensureMonotonic: Use switch statement
Use switch statement rather than if/else for better readability.
Process the most frequent cases first.
* Add max concurrent and current queries engine metrics
This commit adds two metrics to the promql/engine: the
number of max concurrent queries, as configured by the flag, and
the number of current queries being served+blocked in the engine.
This extracts Querier as an instantiateable and closeable object
rather than just defining extending methods of the storage interface.
This improves composability and allows abstracting query transactions,
which can be useful for transaction-level caches, consistent data views,
and encapsulating teardown.
This is based on https://github.com/prometheus/prometheus/pull/1997.
This adds contexts to the relevant Storage methods and already passes
PromQL's new per-query context into the storage's query methods.
The immediate motivation supporting multi-tenancy in Frankenstein, but
this could also be used by Prometheus's normal local storage to support
cancellations and timeouts at some point.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
The current separation between lexer and parser is a bit fuzzy when it
comes to operators, aggregators and other keywords. The lexer already
tries to determine the type of a token, even though that type might
change depending on the context.
This led to the problematic behavior that no tokens known to the lexer
could be used as label names, including operators (and, by, ...),
aggregators (count, quantile, ...) or other keywords (for, offset, ...).
This change additionally checks whether an identifier is one of these
types. We might want to check whether the specific item identification
should be moved from the lexer to the parser.
